Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination

碩士 === 國立高雄第一科技大學 === 風險管理與保險所 === 94 === ABSTRACT "Elder time life insurance policy extends the insurance premium" is the life insurance company important management target, said to the life insurance company, each contract first year institute bears the cost, including raises dials disbu...

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Main Authors: Chien-Hsiang Tsai, 蔡健詳
Other Authors: Shuo-fen Hsu
Format: Others
Language:zh-TW
Published: 2006
Online Access:http://ndltd.ncl.edu.tw/handle/38991633682268169812
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spelling ndltd-TW-094NKIT52180412016-05-20T04:18:03Z http://ndltd.ncl.edu.tw/handle/38991633682268169812 Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination 應用類神經網路建構早期保單停效風險管理模式 Chien-Hsiang Tsai 蔡健詳 碩士 國立高雄第一科技大學 風險管理與保險所 94 ABSTRACT "Elder time life insurance policy extends the insurance premium" is the life insurance company important management target, said to the life insurance company, each contract first year institute bears the cost, including raises dials disbursement sum total and so on the reserve fund, business expense and commission is higher than the insurance premium income, In other words, the new contract chit manages in the first year speaking of the life insurance company is the loss, if client namely terminates an agreement in the second year, to the life insurance company said is the very big loss, only has client continues to pay the insurance premium, the life insurance company can and insures the expense after the reserve fund gradually to spread evenly, will appear the earnings in the future. In recent years the financial commodity changed with each new day, after in addition the financial holding company bill implementation emerging sold the circuit to emerge, has caused the populace has had the change regarding the insurance commodity cognition and the purchase behavior. Although present average each person already had 1.7 life insurances chits, but still had some demands not to obtain satisfies, therefore, insurance company when developed the new customer or carried on sells once more, it is necessary to understand consumer''s thought pattern and the custom. But what reason can create the customer departing? What sign had to be allowed to know in anticipation? Drains before the customer gives the intimate service, reduces the chit to stop the effect the risk. Because the life insurance company generally has the massive client individual materials, therefore this article according to the Data Mining the technical affiliation by the Artificial Neural Network in Self-Organizing Map(SOM) studies, Kaohsiung of, Pingtungng,, the Taitung area clerk the P life insurance company Kaohsiung subsidiary company (calculates in 2005 in 2004 13th month chit continuation rate) to gather chit of material the customer to make the suitable classification to hive off. And analyzes by way of the entire group material in discovers each group the characteristic, and assists the life insurance company by the vision analysis tool to construct the construction "the early chit to stop the effect risk management" the system basis, and so as to adjusts its market area to separate, the human resources and the marketing strategy. Shuo-fen Hsu 許碩芬 2006 學位論文 ; thesis 82 zh-TW
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description 碩士 === 國立高雄第一科技大學 === 風險管理與保險所 === 94 === ABSTRACT "Elder time life insurance policy extends the insurance premium" is the life insurance company important management target, said to the life insurance company, each contract first year institute bears the cost, including raises dials disbursement sum total and so on the reserve fund, business expense and commission is higher than the insurance premium income, In other words, the new contract chit manages in the first year speaking of the life insurance company is the loss, if client namely terminates an agreement in the second year, to the life insurance company said is the very big loss, only has client continues to pay the insurance premium, the life insurance company can and insures the expense after the reserve fund gradually to spread evenly, will appear the earnings in the future. In recent years the financial commodity changed with each new day, after in addition the financial holding company bill implementation emerging sold the circuit to emerge, has caused the populace has had the change regarding the insurance commodity cognition and the purchase behavior. Although present average each person already had 1.7 life insurances chits, but still had some demands not to obtain satisfies, therefore, insurance company when developed the new customer or carried on sells once more, it is necessary to understand consumer''s thought pattern and the custom. But what reason can create the customer departing? What sign had to be allowed to know in anticipation? Drains before the customer gives the intimate service, reduces the chit to stop the effect the risk. Because the life insurance company generally has the massive client individual materials, therefore this article according to the Data Mining the technical affiliation by the Artificial Neural Network in Self-Organizing Map(SOM) studies, Kaohsiung of, Pingtungng,, the Taitung area clerk the P life insurance company Kaohsiung subsidiary company (calculates in 2005 in 2004 13th month chit continuation rate) to gather chit of material the customer to make the suitable classification to hive off. And analyzes by way of the entire group material in discovers each group the characteristic, and assists the life insurance company by the vision analysis tool to construct the construction "the early chit to stop the effect risk management" the system basis, and so as to adjusts its market area to separate, the human resources and the marketing strategy.
author2 Shuo-fen Hsu
author_facet Shuo-fen Hsu
Chien-Hsiang Tsai
蔡健詳
author Chien-Hsiang Tsai
蔡健詳
spellingShingle Chien-Hsiang Tsai
蔡健詳
Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
author_sort Chien-Hsiang Tsai
title Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
title_short Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
title_full Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
title_fullStr Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
title_full_unstemmed Applying Self-Organizing Neural Network Model to Assessing the Risk ofPolicy Early Termination
title_sort applying self-organizing neural network model to assessing the risk ofpolicy early termination
publishDate 2006
url http://ndltd.ncl.edu.tw/handle/38991633682268169812
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